Machine Learning–Based Adaptive Modulation and Coding Design

Zhang, Lin; Wu, Zhiqiang

doi:10.1002/9781119562306.ch9

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…The D2D communication approach will significantly reduce latency and is an ideal solution when extreme low latency is the target like in the case of real-time monitoring and control of in vivo nanomachine applications that require nanocommunication. Other ways of reducing processing time, such as the use of different subcarrier spacing (when OFDM is in use), Transmission Time Intervals (TTIs) [203], [204], and adaptive modulation and coding [205], can also be employed to reduce latency further.…”

Section: Latencymentioning

confidence: 99%

Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey

2023

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Precision medicine (PM) is an innovative medical approach that considers differences in the individuals' omics, medical histories, lifestyles, and environmental information in treating diseases. To fully achieve the envisaged gains of PM, various contemporary and future technologies have to be employed, among which are nanotechnology, sensor network, big data, and artificial intelligence. These technologies and other applications require a communication network that will enable them to work in tandem for the benefit of PM. Hence, communication technology serves as the nervous system of PM, without which the entire system collapses. Therefore, it is essential to explore and determine the candidate communication technology requirements that can guarantee the envisioned gains of PM. To the best of our knowledge, no work exploring how communication technology directly impacts the development and deployment of PM solutions exists. This survey paper is designed to stimulate discussions on PM from the communication engineering perspective. We introduce the fundamentals of PM and the demands in terms of quality of service that each of the enabling technologies of PM places on the communication network. We explore the information in the literature to suggest the ideal metric values of the key performance indicators for the implementation of the different components of PM. The comparative analysis of the suitability of the contemporary and future communication technologies for PM implementation is discussed. Finally, some open research challenges for the candidate communication technologies that will enable the full implementation of PM solutions are highlighted.INDEX TERMS Precision medicine, fourth industrial revolution (4IR) technologies, communication technologies, 5G, 6G.

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Section: Latencymentioning

confidence: 99%

Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey

2023

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“…The common issue with existing AMC systems is either inaccuracy due to model-based approximations or unmanageability due to large-scale lookup tables [34,35]. By contrast, ML is capable of jointly optimizing the AMC-aided system by using a unified non-linear framework [36]. During the past few decades, ML has been widely applied in many fields of study, such as natural language processing (NLP), predictive analytics and computer vision [37,38].…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted adaptive LDPC coded system design and analysis

Xie,

El‐Hajjar,

2023

IET Communications

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This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

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The Survey of Self-driving Car Challenges in Smart City Infrastructures

Kulabukhova¹

2023

Lecture Notes in Computer Science

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Machine Learning–Based Adaptive Modulation and Coding Design

Cited by 5 publications

References 21 publications

Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey

Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey

Machine learning assisted adaptive LDPC coded system design and analysis

The Survey of Self-driving Car Challenges in Smart City Infrastructures

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